Patient Attitudes Toward Telepsychiatry During the COVID-19 Pandemic: A Nationwide, Multisite Survey
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic and its associated movement restrictions forced a rapid and massive transition to telepsychiatry to successfully maintain care continuity. OBJECTIVE: The aim of this study is to examine a large number of patients' experiences of, use of, and attitudes toward telepsychiatry. METHODS: An anonymous 11-question survey was delivered electronically to 14,000 patients receiving telepsychiatry care at 18 participating centers across 11 US states between the months of April and June 2020, including questions about their age and length of service use, as well as experience and satisfaction with telepsychiatry on a 5-point Likert scale. Descriptive statistics were used to analyze and report data. RESULTS: In total, 3070 patients with different age ranges participated. The overall experience using telepsychiatry was either excellent or good for 1189 (82.2%) participants using video and 2312 (81.5%) using telephone. In addition, 1922 (63.6%) patients either agreed or strongly agreed that remote treatment sessions (telephone or video) have been just as helpful as in-person treatment. Lack of commute (n=1406, 46.1%) and flexible scheduling/rescheduling (n=1389, 45.5%) were frequently reported advantages of telepsychiatry, whereas missing the clinic/hospital (n=936, 30.7%) and not feeling as connected to their doctor/nurse/therapist (n=752, 24.6%) were the most frequently reported challenges. After the current pandemic resolves, 1937 (64.2%) respondents either agreed or strongly agreed that they would consider using remote treatment sessions in the future. CONCLUSIONS: Telepsychiatry is very well perceived among a large sample of patients. After the current pandemic resolves, some patients may benefit from continued telepsychiatry, but longitudinal studies are needed to assess impact on clinical outcomes and determine whether patients' perceptions change over time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".